Overview

Dataset statistics

Number of variables21
Number of observations26916
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory168.0 B

Variable types

Categorical10
Numeric10
Boolean1

Warnings

pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdays and 1 other fieldsHigh correlation
emp.var.rate is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
cons.price.idx is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
euribor3m is highly correlated with emp.var.rate and 2 other fieldsHigh correlation
nr.employed is highly correlated with previous and 2 other fieldsHigh correlation
pdays is highly correlated with previousHigh correlation
previous is highly correlated with pdaysHigh correlation
emp.var.rate is highly correlated with cons.price.idx and 2 other fieldsHigh correlation
cons.price.idx is highly correlated with emp.var.rateHigh correlation
euribor3m is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
nr.employed is highly correlated with emp.var.rate and 1 other fieldsHigh correlation
age is highly correlated with pdays and 1 other fieldsHigh correlation
duration is highly correlated with pdays and 1 other fieldsHigh correlation
campaign is highly correlated with pdays and 1 other fieldsHigh correlation
pdays is highly correlated with age and 3 other fieldsHigh correlation
previous is highly correlated with age and 3 other fieldsHigh correlation
emp.var.rate is highly correlated with euribor3mHigh correlation
euribor3m is highly correlated with emp.var.rateHigh correlation
loan is highly correlated with housingHigh correlation
cons.conf.idx is highly correlated with cons.price.idx and 7 other fieldsHigh correlation
cons.price.idx is highly correlated with cons.conf.idx and 6 other fieldsHigh correlation
euribor3m is highly correlated with cons.conf.idx and 5 other fieldsHigh correlation
poutcome is highly correlated with cons.conf.idx and 6 other fieldsHigh correlation
month is highly correlated with cons.conf.idx and 5 other fieldsHigh correlation
subcribed is highly correlated with cons.conf.idx and 1 other fieldsHigh correlation
contact is highly correlated with cons.price.idx and 3 other fieldsHigh correlation
job is highly correlated with education and 1 other fieldsHigh correlation
education is highly correlated with jobHigh correlation
nr.employed is highly correlated with cons.conf.idx and 8 other fieldsHigh correlation
pdays is highly correlated with cons.conf.idx and 3 other fieldsHigh correlation
age is highly correlated with jobHigh correlation
housing is highly correlated with loanHigh correlation
previous is highly correlated with poutcome and 1 other fieldsHigh correlation
emp.var.rate is highly correlated with cons.conf.idx and 6 other fieldsHigh correlation
contact is highly correlated with monthHigh correlation
loan is highly correlated with housingHigh correlation
housing is highly correlated with loanHigh correlation
month is highly correlated with contactHigh correlation
previous has 23042 (85.6%) zeros Zeros

Reproduction

Analysis started2021-08-08 16:12:37.742619
Analysis finished2021-08-08 16:13:22.808811
Duration45.07 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

education
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size210.4 KiB
university.degree
12164 
high.school
9512 
professional.course
5240 

Length

Max length19
Median length17
Mean length15.26898499
Min length11

Characters and Unicode

Total characters410980
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhigh.school
2nd rowhigh.school
3rd rowhigh.school
4th rowprofessional.course
5th rowprofessional.course

Common Values

ValueCountFrequency (%)
university.degree12164
45.2%
high.school9512
35.3%
professional.course5240
19.5%

Length

2021-08-08T23:13:23.012681image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T23:13:23.106344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
university.degree12164
45.2%
high.school9512
35.3%
professional.course5240
19.5%

Most occurring characters

ValueCountFrequency (%)
e59136
14.4%
i39080
9.5%
s37396
 
9.1%
r34808
 
8.5%
o34744
 
8.5%
h28536
 
6.9%
.26916
 
6.5%
g21676
 
5.3%
n17404
 
4.2%
u17404
 
4.2%
Other values (9)93880
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter384064
93.5%
Other Punctuation26916
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e59136
15.4%
i39080
10.2%
s37396
9.7%
r34808
9.1%
o34744
9.0%
h28536
 
7.4%
g21676
 
5.6%
n17404
 
4.5%
u17404
 
4.5%
c14752
 
3.8%
Other values (8)79128
20.6%
Other Punctuation
ValueCountFrequency (%)
.26916
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin384064
93.5%
Common26916
 
6.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e59136
15.4%
i39080
10.2%
s37396
9.7%
r34808
9.1%
o34744
9.0%
h28536
 
7.4%
g21676
 
5.6%
n17404
 
4.5%
u17404
 
4.5%
c14752
 
3.8%
Other values (8)79128
20.6%
Common
ValueCountFrequency (%)
.26916
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII410980
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e59136
14.4%
i39080
9.5%
s37396
 
9.1%
r34808
 
8.5%
o34744
 
8.5%
h28536
 
6.9%
.26916
 
6.5%
g21676
 
5.3%
n17404
 
4.2%
u17404
 
4.2%
Other values (9)93880
22.8%

age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct71
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.80067618
Minimum18
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size210.4 KiB
2021-08-08T23:13:23.216098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile26
Q131
median37
Q345
95-th percentile57
Maximum91
Range73
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.732536154
Coefficient of variation (CV)0.2508341893
Kurtosis0.3457418685
Mean38.80067618
Median Absolute Deviation (MAD)6
Skewness0.7731945695
Sum1044359
Variance94.72225999
MonotonicityNot monotonic
2021-08-08T23:13:23.419989image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311466
 
5.4%
321435
 
5.3%
331376
 
5.1%
301330
 
4.9%
341276
 
4.7%
351262
 
4.7%
361207
 
4.5%
291149
 
4.3%
371092
 
4.1%
39922
 
3.4%
Other values (61)14401
53.5%
ValueCountFrequency (%)
185
 
< 0.1%
194
 
< 0.1%
2037
 
0.1%
2164
 
0.2%
2282
 
0.3%
23159
 
0.6%
24331
1.2%
25423
1.6%
26504
1.9%
27611
2.3%
ValueCountFrequency (%)
912
 
< 0.1%
882
 
< 0.1%
861
 
< 0.1%
852
 
< 0.1%
841
 
< 0.1%
835
< 0.1%
827
< 0.1%
815
< 0.1%
805
< 0.1%
793
< 0.1%

job
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size210.4 KiB
admin.
9442 
technician
5998 
services
3071 
management
2450 
blue-collar
1425 
Other values (7)
4530 

Length

Max length13
Median length8
Mean length8.434351315
Min length6

Characters and Unicode

Total characters227019
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowservices
2nd rowservices
3rd rowservices
4th rowadmin.
5th rowtechnician

Common Values

ValueCountFrequency (%)
admin.9442
35.1%
technician5998
22.3%
services3071
 
11.4%
management2450
 
9.1%
blue-collar1425
 
5.3%
self-employed1051
 
3.9%
entrepreneur979
 
3.6%
retired801
 
3.0%
unemployed663
 
2.5%
student570
 
2.1%
Other values (2)466
 
1.7%

Length

2021-08-08T23:13:23.686812image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin9442
35.1%
technician5998
22.3%
services3071
 
11.4%
management2450
 
9.1%
blue-collar1425
 
5.3%
self-employed1051
 
3.9%
entrepreneur979
 
3.6%
retired801
 
3.0%
unemployed663
 
2.5%
student570
 
2.1%
Other values (2)466
 
1.7%

Most occurring characters

ValueCountFrequency (%)
n29811
13.1%
e29404
13.0%
i25682
11.3%
a22137
9.8%
c16492
 
7.3%
m16428
 
7.2%
d12899
 
5.7%
t11368
 
5.0%
.9442
 
4.2%
r9035
 
4.0%
Other values (14)44321
19.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter215101
94.8%
Other Punctuation9442
 
4.2%
Dash Punctuation2476
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n29811
13.9%
e29404
13.7%
i25682
11.9%
a22137
10.3%
c16492
7.7%
m16428
7.6%
d12899
 
6.0%
t11368
 
5.3%
r9035
 
4.2%
s8135
 
3.8%
Other values (12)33710
15.7%
Other Punctuation
ValueCountFrequency (%)
.9442
100.0%
Dash Punctuation
ValueCountFrequency (%)
-2476
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin215101
94.8%
Common11918
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n29811
13.9%
e29404
13.7%
i25682
11.9%
a22137
10.3%
c16492
7.7%
m16428
7.6%
d12899
 
6.0%
t11368
 
5.3%
r9035
 
4.2%
s8135
 
3.8%
Other values (12)33710
15.7%
Common
ValueCountFrequency (%)
.9442
79.2%
-2476
 
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII227019
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n29811
13.1%
e29404
13.0%
i25682
11.3%
a22137
9.8%
c16492
 
7.3%
m16428
 
7.2%
d12899
 
5.7%
t11368
 
5.0%
.9442
 
4.2%
r9035
 
4.0%
Other values (14)44321
19.5%

marital
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size210.4 KiB
married
14703 
single
8976 
divorced
3186 
unknown
 
51

Length

Max length8
Median length7
Mean length6.784886313
Min length6

Characters and Unicode

Total characters182622
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowsingle

Common Values

ValueCountFrequency (%)
married14703
54.6%
single8976
33.3%
divorced3186
 
11.8%
unknown51
 
0.2%

Length

2021-08-08T23:13:23.953612image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T23:13:24.047811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
married14703
54.6%
single8976
33.3%
divorced3186
 
11.8%
unknown51
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r32592
17.8%
i26865
14.7%
e26865
14.7%
d21075
11.5%
m14703
8.1%
a14703
8.1%
n9129
 
5.0%
s8976
 
4.9%
g8976
 
4.9%
l8976
 
4.9%
Other values (6)9762
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter182622
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r32592
17.8%
i26865
14.7%
e26865
14.7%
d21075
11.5%
m14703
8.1%
a14703
8.1%
n9129
 
5.0%
s8976
 
4.9%
g8976
 
4.9%
l8976
 
4.9%
Other values (6)9762
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin182622
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r32592
17.8%
i26865
14.7%
e26865
14.7%
d21075
11.5%
m14703
8.1%
a14703
8.1%
n9129
 
5.0%
s8976
 
4.9%
g8976
 
4.9%
l8976
 
4.9%
Other values (6)9762
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII182622
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r32592
17.8%
i26865
14.7%
e26865
14.7%
d21075
11.5%
m14703
8.1%
a14703
8.1%
n9129
 
5.0%
s8976
 
4.9%
g8976
 
4.9%
l8976
 
4.9%
Other values (6)9762
 
5.3%

default
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size210.4 KiB
no
23049 
unknown
3864 
yes
 
3

Length

Max length7
Median length2
Mean length2.717900134
Min length2

Characters and Unicode

Total characters73155
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowno
3rd rowno
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no23049
85.6%
unknown3864
 
14.4%
yes3
 
< 0.1%

Length

2021-08-08T23:13:24.251692image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T23:13:24.330875image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no23049
85.6%
unknown3864
 
14.4%
yes3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n34641
47.4%
o26913
36.8%
u3864
 
5.3%
k3864
 
5.3%
w3864
 
5.3%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter73155
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n34641
47.4%
o26913
36.8%
u3864
 
5.3%
k3864
 
5.3%
w3864
 
5.3%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin73155
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n34641
47.4%
o26913
36.8%
u3864
 
5.3%
k3864
 
5.3%
w3864
 
5.3%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII73155
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n34641
47.4%
o26913
36.8%
u3864
 
5.3%
k3864
 
5.3%
w3864
 
5.3%
y3
 
< 0.1%
e3
 
< 0.1%
s3
 
< 0.1%

housing
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size210.4 KiB
yes
14269 
no
12025 
unknown
 
622

Length

Max length7
Median length3
Mean length2.645675435
Min length2

Characters and Unicode

Total characters71211
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowyes
3rd rowno
4th rowno
5th rowyes

Common Values

ValueCountFrequency (%)
yes14269
53.0%
no12025
44.7%
unknown622
 
2.3%

Length

2021-08-08T23:13:24.565473image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T23:13:24.631469image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
yes14269
53.0%
no12025
44.7%
unknown622
 
2.3%

Most occurring characters

ValueCountFrequency (%)
y14269
20.0%
e14269
20.0%
s14269
20.0%
n13891
19.5%
o12647
17.8%
u622
 
0.9%
k622
 
0.9%
w622
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter71211
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y14269
20.0%
e14269
20.0%
s14269
20.0%
n13891
19.5%
o12647
17.8%
u622
 
0.9%
k622
 
0.9%
w622
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin71211
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
y14269
20.0%
e14269
20.0%
s14269
20.0%
n13891
19.5%
o12647
17.8%
u622
 
0.9%
k622
 
0.9%
w622
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII71211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y14269
20.0%
e14269
20.0%
s14269
20.0%
n13891
19.5%
o12647
17.8%
u622
 
0.9%
k622
 
0.9%
w622
 
0.9%

loan
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size210.4 KiB
no
22128 
yes
4166 
unknown
 
622

Length

Max length7
Median length2
Mean length2.270322485
Min length2

Characters and Unicode

Total characters61108
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowyes
4th rowno
5th rowno

Common Values

ValueCountFrequency (%)
no22128
82.2%
yes4166
 
15.5%
unknown622
 
2.3%

Length

2021-08-08T23:13:24.816355image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T23:13:24.878736image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
no22128
82.2%
yes4166
 
15.5%
unknown622
 
2.3%

Most occurring characters

ValueCountFrequency (%)
n23994
39.3%
o22750
37.2%
y4166
 
6.8%
e4166
 
6.8%
s4166
 
6.8%
u622
 
1.0%
k622
 
1.0%
w622
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter61108
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n23994
39.3%
o22750
37.2%
y4166
 
6.8%
e4166
 
6.8%
s4166
 
6.8%
u622
 
1.0%
k622
 
1.0%
w622
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin61108
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n23994
39.3%
o22750
37.2%
y4166
 
6.8%
e4166
 
6.8%
s4166
 
6.8%
u622
 
1.0%
k622
 
1.0%
w622
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII61108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n23994
39.3%
o22750
37.2%
y4166
 
6.8%
e4166
 
6.8%
s4166
 
6.8%
u622
 
1.0%
k622
 
1.0%
w622
 
1.0%

contact
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size210.4 KiB
cellular
18054 
telephone
8862 

Length

Max length9
Median length8
Mean length8.329246545
Min length8

Characters and Unicode

Total characters224190
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular18054
67.1%
telephone8862
32.9%

Length

2021-08-08T23:13:25.113934image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T23:13:25.208071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
cellular18054
67.1%
telephone8862
32.9%

Most occurring characters

ValueCountFrequency (%)
l63024
28.1%
e44640
19.9%
c18054
 
8.1%
u18054
 
8.1%
a18054
 
8.1%
r18054
 
8.1%
t8862
 
4.0%
p8862
 
4.0%
h8862
 
4.0%
o8862
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter224190
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l63024
28.1%
e44640
19.9%
c18054
 
8.1%
u18054
 
8.1%
a18054
 
8.1%
r18054
 
8.1%
t8862
 
4.0%
p8862
 
4.0%
h8862
 
4.0%
o8862
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Latin224190
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l63024
28.1%
e44640
19.9%
c18054
 
8.1%
u18054
 
8.1%
a18054
 
8.1%
r18054
 
8.1%
t8862
 
4.0%
p8862
 
4.0%
h8862
 
4.0%
o8862
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII224190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l63024
28.1%
e44640
19.9%
c18054
 
8.1%
u18054
 
8.1%
a18054
 
8.1%
r18054
 
8.1%
t8862
 
4.0%
p8862
 
4.0%
h8862
 
4.0%
o8862
 
4.0%

month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size210.4 KiB
may
8044 
aug
4951 
jul
4443 
jun
3264 
nov
3000 
Other values (5)
3214 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80748
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may8044
29.9%
aug4951
18.4%
jul4443
16.5%
jun3264
12.1%
nov3000
 
11.1%
apr1725
 
6.4%
oct521
 
1.9%
sep426
 
1.6%
mar410
 
1.5%
dec132
 
0.5%

Length

2021-08-08T23:13:25.474867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T23:13:25.569006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
may8044
29.9%
aug4951
18.4%
jul4443
16.5%
jun3264
12.1%
nov3000
 
11.1%
apr1725
 
6.4%
oct521
 
1.9%
sep426
 
1.6%
mar410
 
1.5%
dec132
 
0.5%

Most occurring characters

ValueCountFrequency (%)
a15130
18.7%
u12658
15.7%
m8454
10.5%
y8044
10.0%
j7707
9.5%
n6264
7.8%
g4951
 
6.1%
l4443
 
5.5%
o3521
 
4.4%
v3000
 
3.7%
Other values (7)6576
8.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter80748
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a15130
18.7%
u12658
15.7%
m8454
10.5%
y8044
10.0%
j7707
9.5%
n6264
7.8%
g4951
 
6.1%
l4443
 
5.5%
o3521
 
4.4%
v3000
 
3.7%
Other values (7)6576
8.1%

Most occurring scripts

ValueCountFrequency (%)
Latin80748
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a15130
18.7%
u12658
15.7%
m8454
10.5%
y8044
10.0%
j7707
9.5%
n6264
7.8%
g4951
 
6.1%
l4443
 
5.5%
o3521
 
4.4%
v3000
 
3.7%
Other values (7)6576
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII80748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a15130
18.7%
u12658
15.7%
m8454
10.5%
y8044
10.0%
j7707
9.5%
n6264
7.8%
g4951
 
6.1%
l4443
 
5.5%
o3521
 
4.4%
v3000
 
3.7%
Other values (7)6576
8.1%

day_of_week
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size210.4 KiB
thu
5663 
mon
5659 
tue
5258 
wed
5243 
fri
5093 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters80748
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowmon
5th rowmon

Common Values

ValueCountFrequency (%)
thu5663
21.0%
mon5659
21.0%
tue5258
19.5%
wed5243
19.5%
fri5093
18.9%

Length

2021-08-08T23:13:25.851404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T23:13:25.934020image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
thu5663
21.0%
mon5659
21.0%
tue5258
19.5%
wed5243
19.5%
fri5093
18.9%

Most occurring characters

ValueCountFrequency (%)
t10921
13.5%
u10921
13.5%
e10501
13.0%
h5663
7.0%
m5659
7.0%
o5659
7.0%
n5659
7.0%
w5243
6.5%
d5243
6.5%
f5093
6.3%
Other values (2)10186
12.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter80748
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t10921
13.5%
u10921
13.5%
e10501
13.0%
h5663
7.0%
m5659
7.0%
o5659
7.0%
n5659
7.0%
w5243
6.5%
d5243
6.5%
f5093
6.3%
Other values (2)10186
12.6%

Most occurring scripts

ValueCountFrequency (%)
Latin80748
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t10921
13.5%
u10921
13.5%
e10501
13.0%
h5663
7.0%
m5659
7.0%
o5659
7.0%
n5659
7.0%
w5243
6.5%
d5243
6.5%
f5093
6.3%
Other values (2)10186
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII80748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t10921
13.5%
u10921
13.5%
e10501
13.0%
h5663
7.0%
m5659
7.0%
o5659
7.0%
n5659
7.0%
w5243
6.5%
d5243
6.5%
f5093
6.3%
Other values (2)10186
12.6%

duration
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1396
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean255.8320701
Minimum0
Maximum4918
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size210.4 KiB
2021-08-08T23:13:26.070926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1101
median177
Q3316
95-th percentile747
Maximum4918
Range4918
Interquartile range (IQR)215

Descriptive statistics

Standard deviation259.1917276
Coefficient of variation (CV)1.013132276
Kurtosis21.5648321
Mean255.8320701
Median Absolute Deviation (MAD)92
Skewness3.352341076
Sum6885976
Variance67180.35166
MonotonicityNot monotonic
2021-08-08T23:13:26.243580image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90130
 
0.5%
73116
 
0.4%
72115
 
0.4%
104114
 
0.4%
136114
 
0.4%
124114
 
0.4%
96113
 
0.4%
109109
 
0.4%
87108
 
0.4%
126108
 
0.4%
Other values (1386)25775
95.8%
ValueCountFrequency (%)
03
 
< 0.1%
12
 
< 0.1%
32
 
< 0.1%
49
 
< 0.1%
518
 
0.1%
632
0.1%
734
0.1%
842
0.2%
952
0.2%
1051
0.2%
ValueCountFrequency (%)
49181
< 0.1%
37851
< 0.1%
36431
< 0.1%
36311
< 0.1%
34221
< 0.1%
33221
< 0.1%
32841
< 0.1%
32531
< 0.1%
31831
< 0.1%
30761
< 0.1%

campaign
Real number (ℝ≥0)

HIGH CORRELATION

Distinct41
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.570032694
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size210.4 KiB
2021-08-08T23:13:26.447477image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.76312996
Coefficient of variation (CV)1.075134167
Kurtosis38.87088032
Mean2.570032694
Median Absolute Deviation (MAD)1
Skewness4.800349514
Sum69175
Variance7.634887177
MonotonicityNot monotonic
2021-08-08T23:13:26.917379image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
111537
42.9%
26896
25.6%
33453
 
12.8%
41737
 
6.5%
51065
 
4.0%
6638
 
2.4%
7408
 
1.5%
8263
 
1.0%
9189
 
0.7%
10152
 
0.6%
Other values (31)578
 
2.1%
ValueCountFrequency (%)
111537
42.9%
26896
25.6%
33453
 
12.8%
41737
 
6.5%
51065
 
4.0%
6638
 
2.4%
7408
 
1.5%
8263
 
1.0%
9189
 
0.7%
10152
 
0.6%
ValueCountFrequency (%)
561
 
< 0.1%
432
< 0.1%
421
 
< 0.1%
402
< 0.1%
391
 
< 0.1%
371
 
< 0.1%
352
< 0.1%
343
< 0.1%
334
< 0.1%
324
< 0.1%

pdays
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct25
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean957.9719126
Minimum0
Maximum999
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size210.4 KiB
2021-08-08T23:13:27.058823image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile999
Q1999
median999
Q3999
95-th percentile999
Maximum999
Range999
Interquartile range (IQR)0

Descriptive statistics

Standard deviation197.6440734
Coefficient of variation (CV)0.2063151025
Kurtosis19.25337475
Mean957.9719126
Median Absolute Deviation (MAD)0
Skewness-4.609941553
Sum25784772
Variance39063.17974
MonotonicityNot monotonic
2021-08-08T23:13:27.199817image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
99925804
95.9%
3325
 
1.2%
6308
 
1.1%
491
 
0.3%
248
 
0.2%
745
 
0.2%
940
 
0.1%
1239
 
0.1%
1034
 
0.1%
532
 
0.1%
Other values (15)150
 
0.6%
ValueCountFrequency (%)
012
 
< 0.1%
120
 
0.1%
248
 
0.2%
3325
1.2%
491
 
0.3%
532
 
0.1%
6308
1.1%
745
 
0.2%
815
 
0.1%
940
 
0.1%
ValueCountFrequency (%)
99925804
95.9%
271
 
< 0.1%
261
 
< 0.1%
223
 
< 0.1%
212
 
< 0.1%
193
 
< 0.1%
185
 
< 0.1%
176
 
< 0.1%
166
 
< 0.1%
1518
 
0.1%

previous
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1844627731
Minimum0
Maximum7
Zeros23042
Zeros (%)85.6%
Negative0
Negative (%)0.0%
Memory size210.4 KiB
2021-08-08T23:13:27.325231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5153105375
Coefficient of variation (CV)2.793574709
Kurtosis19.55102419
Mean0.1844627731
Median Absolute Deviation (MAD)0
Skewness3.775171023
Sum4965
Variance0.2655449501
MonotonicityNot monotonic
2021-08-08T23:13:27.451013image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
023042
85.6%
13112
 
11.6%
2529
 
2.0%
3163
 
0.6%
450
 
0.2%
515
 
0.1%
64
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
023042
85.6%
13112
 
11.6%
2529
 
2.0%
3163
 
0.6%
450
 
0.2%
515
 
0.1%
64
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
64
 
< 0.1%
515
 
0.1%
450
 
0.2%
3163
 
0.6%
2529
 
2.0%
13112
 
11.6%
023042
85.6%

poutcome
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size210.4 KiB
nonexistent
23042 
failure
2864 
success
 
1010

Length

Max length11
Median length11
Mean length10.42428295
Min length7

Characters and Unicode

Total characters280580
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent23042
85.6%
failure2864
 
10.6%
success1010
 
3.8%

Length

2021-08-08T23:13:27.717409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T23:13:27.827162image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent23042
85.6%
failure2864
 
10.6%
success1010
 
3.8%

Most occurring characters

ValueCountFrequency (%)
n69126
24.6%
e49958
17.8%
t46084
16.4%
s26072
 
9.3%
i25906
 
9.2%
o23042
 
8.2%
x23042
 
8.2%
u3874
 
1.4%
f2864
 
1.0%
a2864
 
1.0%
Other values (3)7748
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter280580
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n69126
24.6%
e49958
17.8%
t46084
16.4%
s26072
 
9.3%
i25906
 
9.2%
o23042
 
8.2%
x23042
 
8.2%
u3874
 
1.4%
f2864
 
1.0%
a2864
 
1.0%
Other values (3)7748
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin280580
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n69126
24.6%
e49958
17.8%
t46084
16.4%
s26072
 
9.3%
i25906
 
9.2%
o23042
 
8.2%
x23042
 
8.2%
u3874
 
1.4%
f2864
 
1.0%
a2864
 
1.0%
Other values (3)7748
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII280580
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n69126
24.6%
e49958
17.8%
t46084
16.4%
s26072
 
9.3%
i25906
 
9.2%
o23042
 
8.2%
x23042
 
8.2%
u3874
 
1.4%
f2864
 
1.0%
a2864
 
1.0%
Other values (3)7748
 
2.8%

emp.var.rate
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03275746768
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative11775
Negative (%)43.7%
Memory size210.4 KiB
2021-08-08T23:13:27.921296image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.593991688
Coefficient of variation (CV)48.66040635
Kurtosis-1.114877951
Mean0.03275746768
Median Absolute Deviation (MAD)0.3
Skewness-0.6773075169
Sum881.7
Variance2.540809502
MonotonicityNot monotonic
2021-08-08T23:13:28.037564image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.410770
40.0%
-1.85808
21.6%
1.14371
16.2%
-0.12692
 
10.0%
-2.91311
 
4.9%
-3.4757
 
2.8%
-1.7577
 
2.1%
-1.1498
 
1.9%
-3125
 
0.5%
-0.27
 
< 0.1%
ValueCountFrequency (%)
-3.4757
 
2.8%
-3125
 
0.5%
-2.91311
 
4.9%
-1.85808
21.6%
-1.7577
 
2.1%
-1.1498
 
1.9%
-0.27
 
< 0.1%
-0.12692
 
10.0%
1.14371
16.2%
1.410770
40.0%
ValueCountFrequency (%)
1.410770
40.0%
1.14371
16.2%
-0.12692
 
10.0%
-0.27
 
< 0.1%
-1.1498
 
1.9%
-1.7577
 
2.1%
-1.85808
21.6%
-2.91311
 
4.9%
-3125
 
0.5%
-3.4757
 
2.8%

cons.price.idx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.54073763
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size210.4 KiB
2021-08-08T23:13:28.156843image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.649
Q193.075
median93.444
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.5749010311
Coefficient of variation (CV)0.00614599634
Kurtosis-0.7504257313
Mean93.54073763
Median Absolute Deviation (MAD)0.481
Skewness-0.1600115773
Sum2517742.494
Variance0.3305111956
MonotonicityNot monotonic
2021-08-08T23:13:28.297840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.9944371
16.2%
93.4444194
15.6%
93.9184073
15.1%
92.8933516
13.1%
93.22637
9.8%
94.4652503
9.3%
93.0751616
 
6.0%
92.201590
 
2.2%
92.963574
 
2.1%
92.431307
 
1.1%
Other values (16)2535
9.4%
ValueCountFrequency (%)
92.201590
 
2.2%
92.379188
 
0.7%
92.431307
 
1.1%
92.469147
 
0.5%
92.649262
 
1.0%
92.713125
 
0.5%
92.7567
 
< 0.1%
92.843212
 
0.8%
92.8933516
13.1%
92.963574
 
2.1%
ValueCountFrequency (%)
94.767101
 
0.4%
94.601159
 
0.6%
94.4652503
9.3%
94.215223
 
0.8%
94.199238
 
0.9%
94.055187
 
0.7%
94.027167
 
0.6%
93.9944371
16.2%
93.9184073
15.1%
93.876157
 
0.6%

cons.conf.idx
Real number (ℝ)

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.34590578
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative26916
Negative (%)100.0%
Memory size210.4 KiB
2021-08-08T23:13:28.423240image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-31.4
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.699283382
Coefficient of variation (CV)-0.1164748514
Kurtosis-0.3895031794
Mean-40.34590578
Median Absolute Deviation (MAD)4.4
Skewness0.254657384
Sum-1085950.4
Variance22.08326431
MonotonicityNot monotonic
2021-08-08T23:13:28.538538image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.44371
16.2%
-36.14194
15.6%
-42.74073
15.1%
-46.23516
13.1%
-422637
9.8%
-41.82503
9.3%
-47.11616
 
6.0%
-31.4590
 
2.2%
-40.8574
 
2.1%
-26.9307
 
1.1%
Other values (16)2535
9.4%
ValueCountFrequency (%)
-50.8101
 
0.4%
-50212
 
0.8%
-49.5159
 
0.6%
-47.11616
 
6.0%
-46.23516
13.1%
-45.97
 
< 0.1%
-42.74073
15.1%
-422637
9.8%
-41.82503
9.3%
-40.8574
 
2.1%
ValueCountFrequency (%)
-26.9307
 
1.1%
-29.8188
 
0.7%
-30.1262
 
1.0%
-31.4590
 
2.2%
-33125
 
0.5%
-33.6147
 
0.5%
-34.6109
 
0.4%
-34.8198
 
0.7%
-36.14194
15.6%
-36.44371
16.2%

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct309
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.574311785
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size210.4 KiB
2021-08-08T23:13:28.674466image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.755
Q11.334
median4.857
Q34.962
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.628

Descriptive statistics

Standard deviation1.751243256
Coefficient of variation (CV)0.4899525731
Kurtosis-1.468188562
Mean3.574311785
Median Absolute Deviation (MAD)0.109
Skewness-0.6608864608
Sum96206.176
Variance3.066852942
MonotonicityNot monotonic
2021-08-08T23:13:28.799844image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.9631777
 
6.6%
4.9621703
 
6.3%
4.8571636
 
6.1%
4.9611114
 
4.1%
4.964950
 
3.5%
4.965854
 
3.2%
4.968743
 
2.8%
1.405741
 
2.8%
4.856656
 
2.4%
4.076596
 
2.2%
Other values (299)16146
60.0%
ValueCountFrequency (%)
0.6345
 
< 0.1%
0.63526
0.1%
0.6368
 
< 0.1%
0.6372
 
< 0.1%
0.6385
 
< 0.1%
0.6396
 
< 0.1%
0.649
 
< 0.1%
0.64219
0.1%
0.64312
< 0.1%
0.64426
0.1%
ValueCountFrequency (%)
5.0456
 
< 0.1%
57
 
< 0.1%
4.97125
 
0.5%
4.968743
2.8%
4.967422
 
1.6%
4.966488
 
1.8%
4.965854
3.2%
4.964950
3.5%
4.9631777
6.6%
4.9621703
6.3%

nr.employed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5165.201445
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size210.4 KiB
2021-08-08T23:13:28.909684image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5008.7
Q15099.1
median5195.8
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation74.60155888
Coefficient of variation (CV)0.01444310734
Kurtosis-0.1293520099
Mean5165.201445
Median Absolute Deviation (MAD)32.3
Skewness-1.012022388
Sum139026562.1
Variance5565.392588
MonotonicityNot monotonic
2021-08-08T23:13:29.003811image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.110770
40.0%
5099.15344
19.9%
51914371
16.2%
5195.82692
 
10.0%
5076.21311
 
4.9%
5017.5757
 
2.8%
4991.6577
 
2.1%
4963.6498
 
1.9%
5008.7464
 
1.7%
5023.5125
 
0.5%
ValueCountFrequency (%)
4963.6498
 
1.9%
4991.6577
 
2.1%
5008.7464
 
1.7%
5017.5757
 
2.8%
5023.5125
 
0.5%
5076.21311
 
4.9%
5099.15344
19.9%
5176.37
 
< 0.1%
51914371
16.2%
5195.82692
10.0%
ValueCountFrequency (%)
5228.110770
40.0%
5195.82692
 
10.0%
51914371
16.2%
5176.37
 
< 0.1%
5099.15344
19.9%
5076.21311
 
4.9%
5023.5125
 
0.5%
5017.5757
 
2.8%
5008.7464
 
1.7%
4991.6577
 
2.1%

subcribed
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
False
23621 
True
3295 
ValueCountFrequency (%)
False23621
87.8%
True3295
 
12.2%
2021-08-08T23:13:29.097937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Interactions

2021-08-08T23:13:06.013401image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:06.170056image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:06.327218image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:06.468243image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:06.625328image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:06.766360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:07.064061image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:07.267982image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:07.456257image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:07.628906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:07.817174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:08.005465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:08.177723image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:08.350373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:08.507403image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:08.701282image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:08.867465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:09.001827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:09.134221image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:09.275223image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:09.432263image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:09.588877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:09.777166image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:09.918571image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:10.043952image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:10.216606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:10.388852image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:10.529754image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:10.686372image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:10.843395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:11.016054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:11.172679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:11.329728image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:11.455126image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:11.612173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:11.768816image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:11.907213image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:12.066859image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:12.223375image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:12.380019image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:12.508409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:12.678085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:13.023404image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:13.211666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:13.368318image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:13.525329image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:13.666373image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:13.807432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:13.964456image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:14.136592image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:14.293271image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:14.465974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:14.591385image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:14.732799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:14.858227image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:15.030864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:15.187482image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:15.313864image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:15.469873image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:15.614382image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:15.736685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:15.877169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:16.034204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:16.175231image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:16.332252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:16.488861image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:16.630252image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:16.755625image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:16.880988image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:17.022381image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:17.163415image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:17.336029image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:17.492671image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:17.649171image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:17.805793image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:17.947203image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:18.072584image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:18.229618image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:18.386250image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:18.527653image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:18.653045image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:18.809664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:18.935450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:19.076463image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:19.220974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:19.358877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:19.483743image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:19.813149image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:19.939006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:20.158475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:20.377974image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:20.581860image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:20.738886image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:20.864276image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:21.024409image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:21.177928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:21.350079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:21.506695image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:21.632492image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-08T23:13:21.757905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-08T23:13:29.192079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-08T23:13:29.411602image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-08T23:13:29.647145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-08T23:13:29.881800image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-08T23:13:30.165098image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-08T23:13:22.055979image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-08T23:13:22.627432image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

educationagejobmaritaldefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedsubcribed
0high.school57servicesmarriedunknownnonotelephonemaymon14919990nonexistent1.193.994-36.44.8575191.0no
1high.school37servicesmarriednoyesnotelephonemaymon22619990nonexistent1.193.994-36.44.8575191.0no
2high.school56servicesmarriednonoyestelephonemaymon30719990nonexistent1.193.994-36.44.8575191.0no
3professional.course59admin.marriednononotelephonemaymon13919990nonexistent1.193.994-36.44.8575191.0no
4professional.course24techniciansinglenoyesnotelephonemaymon38019990nonexistent1.193.994-36.44.8575191.0no
5high.school25servicessinglenoyesnotelephonemaymon5019990nonexistent1.193.994-36.44.8575191.0no
6high.school25servicessinglenoyesnotelephonemaymon22219990nonexistent1.193.994-36.44.8575191.0no
7high.school29blue-collarsinglenonoyestelephonemaymon13719990nonexistent1.193.994-36.44.8575191.0no
8high.school30unemployedmarriednononotelephonemaymon3819990nonexistent1.193.994-36.44.8575191.0no
9high.school55retiredsinglenoyesnotelephonemaymon34219990nonexistent1.193.994-36.44.8575191.0no

Last rows

educationagejobmaritaldefaulthousingloancontactmonthday_of_weekdurationcampaignpdayspreviouspoutcomeemp.var.ratecons.price.idxcons.conf.idxeuribor3mnr.employedsubcribed
26906professional.course57retiredmarriednoyesnocellularnovthu12469990nonexistent-1.194.767-50.81.0314963.6no
26907university.degree62retiredmarriednononocellularnovthu483263success-1.194.767-50.81.0314963.6yes
26908professional.course64retireddivorcednoyesnocellularnovfri15139990nonexistent-1.194.767-50.81.0284963.6no
26909university.degree36admin.marriednononocellularnovfri25429990nonexistent-1.194.767-50.81.0284963.6no
26910university.degree37admin.marriednoyesnocellularnovfri28119990nonexistent-1.194.767-50.81.0284963.6yes
26911professional.course73retiredmarriednoyesnocellularnovfri33419990nonexistent-1.194.767-50.81.0284963.6yes
26912professional.course46blue-collarmarriednononocellularnovfri38319990nonexistent-1.194.767-50.81.0284963.6no
26913university.degree56retiredmarriednoyesnocellularnovfri18929990nonexistent-1.194.767-50.81.0284963.6no
26914professional.course44technicianmarriednononocellularnovfri44219990nonexistent-1.194.767-50.81.0284963.6yes
26915professional.course74retiredmarriednoyesnocellularnovfri23939991failure-1.194.767-50.81.0284963.6no